added cm_logo_skf.py and placeholder for splits
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4 changed files with 254 additions and 49 deletions
120
scripts/ml/combined_model/cm_logo_skf.py
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120
scripts/ml/combined_model/cm_logo_skf.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jun 29 19:44:06 2022
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@author: tanu
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"""
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import sys, os
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import pandas as pd
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import numpy as np
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import re
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
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sys.path
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###############################################################################
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#====================
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# Import ML functions
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#====================
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from ml_data_combined import *
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from MultClfs_logo_skf import *
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#from GetMLData import *
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#from SplitTTS import *
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skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True,**rs)
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#logo = LeaveOneGroupOut()
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#%%
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def CMLogoSkf(combined_df
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, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
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, bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
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, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
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, target_var = 'dst_mode'
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, gene_group = 'gene_name'
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, std_gene_omit = []
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):
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for bts_gene in bts_genes:
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print('\n BTS gene:', bts_gene)
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tr_gene_omit = std_gene_omit + [bts_gene]
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n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
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#n_total_genes = (len(bts_genes) - len(std_gene_omit))
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n_total_genes = len(all_genes)
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training_genesL = std_gene_omit + list(set(bts_genes) - set(tr_gene_omit))
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#training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
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print('\nTotal genes: ', n_total_genes
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,'\nTraining on:', n_tr_genes
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,'\nTraining on genes:', training_genesL
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, '\nOmitted genes:', tr_gene_omit
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, '\nBlind test gene:', bts_gene)
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tts_split_type = "logoBT_" + bts_gene
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outFile = "/home/tanu/git/Data/ml_combined/" + str(n_tr_genes+1) + "genes_" + tts_split_type + ".csv"
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print(outFile)
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#-------
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# training
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#------
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cm_training_df = combined_df[~combined_df['gene_name'].isin(tr_gene_omit)]
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cm_X = cm_training_df.drop(cols_to_drop, axis=1, inplace=False)
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#cm_y = cm_training_df.loc[:,'dst_mode']
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cm_y = cm_training_df.loc[:, target_var]
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gene_group = cm_training_df.loc[:,'gene_name']
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print('\nTraining data dim:', cm_X.shape
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, '\nTraining Target dim:', cm_y.shape)
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if all(cm_X.columns.isin(cols_to_drop) == False):
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print('\nChecked training df does NOT have Target var')
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else:
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sys.exit('\nFAIL: training data contains Target var')
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#---------------
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# BTS: genes
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#---------------
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cm_test_df = combined_df[combined_df['gene_name'].isin([bts_gene])]
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cm_bts_X = cm_test_df.drop(cols_to_drop, axis = 1, inplace = False)
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#cm_bts_y = cm_test_df.loc[:, 'dst_mode']
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cm_bts_y = cm_test_df.loc[:, target_var]
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print('\nTraining data dim:', cm_bts_X.shape
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, '\nTraining Target dim:', cm_bts_y.shape)
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#%%:Running Multiple models on LOGO with SKF
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cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X
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, target = cm_y
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, group = 'none'
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, sel_cv = skf_cv
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, blind_test_df = cm_bts_X
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, blind_test_target = cm_bts_y
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, tts_split_type = tts_split_type
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, resampling_type = 'none' # default
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, add_cm = True
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, add_yn = True
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, var_type = 'mixed'
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, run_blind_test = True
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = 10
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)
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cD3_v2.to_csv(outFile)
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#%%
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CMLogoSkf(combined_df)
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CMLogoSkf(combined_df, std_gene_omit=['alr'])
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107
scripts/ml/combined_model/cm_ml_iterator.py
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107
scripts/ml/combined_model/cm_ml_iterator.py
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@ -0,0 +1,107 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jun 29 20:29:36 2022
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@author: tanu
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"""
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import sys, os
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import pandas as pd
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import numpy as np
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import re
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
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sys.path
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###############################################################################
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#====================
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# Import ML functions
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#====================
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from MultClfs import *
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from GetMLData import *
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from SplitTTS import *
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# param dict for getmldata()
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combined_model_paramD = {'data_combined_model' : False
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, 'use_or' : False
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, 'omit_all_genomic_features': False
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, 'write_maskfile' : False
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, 'write_outfile' : False }
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###############################################################################
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#ml_genes = ["pncA", "embB", "katG", "rpoB", "gid"]
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ml_gene_drugD = {'pncA' : 'pyrazinamide'
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, 'embB' : 'ethambutol'
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, 'katG' : 'isoniazid'
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, 'rpoB' : 'rifampicin'
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, 'gid' : 'streptomycin'
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}
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gene_dataD={}
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split_types = ['70_30', '80_20', 'sl']
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split_data_types = ['actual', 'complete']
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for gene, drug in ml_gene_drugD.items():
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print ('\nGene:', gene
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, '\nDrug:', drug)
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gene_low = gene.lower()
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gene_dataD[gene_low] = getmldata(gene, drug
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, data_combined_model = False # this means it doesn't include 'gene_name' as a feauture as a single gene-target shouldn't have it.
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, use_or = False
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, omit_all_genomic_features = False
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, write_maskfile = False
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, write_outfile = False)
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for split_type in split_types:
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for data_type in split_data_types:
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out_filename = (gene.lower()+'_'+split_type+'_'+data_type+'.csv')
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tempD=split_tts(gene_dataD[gene_low]
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, data_type = data_type
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, split_type = split_type
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, oversampling = True
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, dst_colname = 'dst'
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, target_colname = 'dst_mode'
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, include_gene_name = True
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)
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paramD = {
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'baseline_paramD': { 'input_df' : tempD['X']
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, 'target' : tempD['y']
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, 'var_type' : 'mixed'
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, 'resampling_type': 'none'}
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, 'smnc_paramD': { 'input_df' : tempD['X_smnc']
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, 'target' : tempD['y_smnc']
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'smnc'}
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, 'ros_paramD': { 'input_df' : tempD['X_ros']
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, 'target' : tempD['y_ros']
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'ros'}
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, 'rus_paramD' : { 'input_df' : tempD['X_rus']
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, 'target' : tempD['y_rus']
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, 'var_type' : 'mixed'
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, 'resampling_type' : 'rus'}
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, 'rouC_paramD' : { 'input_df' : tempD['X_rouC']
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, 'target' : tempD['y_rouC']
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, 'var_type' : 'mixed'
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, 'resampling_type': 'rouC'}
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}
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mmDD = {}
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for k, v in paramD.items():
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scoresD = MultModelsCl(**paramD[k]
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, tts_split_type = split_type
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, skf_cv = skf_cv
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, blind_test_df = tempD['X_bts']
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, blind_test_target = tempD['y_bts']
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, add_cm = True
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, add_yn = True
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, return_formatted_output = True)
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mmDD[k] = scoresD
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# Extracting the dfs from within the dict and concatenating to output as one df
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for k, v in mmDD.items():
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out_wf= pd.concat(mmDD, ignore_index = True)
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out_wf_f = out_wf.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
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out_wf_f.to_csv(('/home/tanu/git/Data/ml_combined/genes/'+out_filename), index = False)
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@ -89,14 +89,7 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'jcc' : make_scorer(jaccard_score)
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})
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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logo = LeaveOneGroupOut()
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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@ -160,7 +153,10 @@ def MultModelsCl_logo_skf(input_df
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, add_yn = True # adds target var class numbers
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, var_type = ['numerical', 'categorical','mixed']
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, run_blind_test = True
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, return_formatted_output = True):
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = 10
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, ):
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'''
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@ param input_df: input features
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@ -179,10 +175,24 @@ def MultModelsCl_logo_skf(input_df
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Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
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'''
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# if group == 'none':
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# sel_cv = skf_cv
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# else:
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# group = 'none'
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#%% Func globals
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rs = {'random_state': random_state}
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njobs = {'n_jobs': n_jobs}
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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logo = LeaveOneGroupOut()
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# select CV type:
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if group == 'none':
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sel_cv = skf_cv
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else:
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sel_cv = logo
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#======================================================
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# Determine categorical and numerical features
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#======================================================
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@ -63,40 +63,8 @@ else:
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, '\nGot:', len(common_cols))
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colnames_combined_df = combined_df.columns
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if 'gene_name' in colnames_combined_df:
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print("\nGene name included")
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else:
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('\nGene name NOT included')
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##############################################################################
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#%% split_tts(): func params
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# (ml_input_data
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# , data_type = ['actual', 'complete']
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# , split_type = ['70_30', '80_20', 'sl']
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# , oversampling = True
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# , dst_colname = 'dst'# determine how to subset the actual vs reverse data
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# , target_colname = 'dst_mode'
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# , include_gene_name = True
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# , k_smote = 5)
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#%% split data into different data types
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# #===================
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# # 70/30
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# #===================
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# # actual
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# tts_7030_paramD = {'data_type' : 'actual'
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# , 'split_type' : '70_30'}
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# # complete
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# tts_cd_7030_paramD = {'data_type' : 'complete'
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# , 'split_type' : '70_30'}
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# # call split_tts()
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# data_CM_7030D = split_tts(ml_input_data = combined_df
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# , **tts_7030_paramD
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# , oversampling = True
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# , dst_colname = 'dst'
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# , target_colname = 'dst_mode'
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# , include_gene_name = False) # when not doing leave one group out
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# data_cd_CM_7030D = split_tts(ml_input_data = combined_df
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# , **tts_cd_7030_paramD
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# , oversampling = True
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# , dst_colname = 'dst'
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# , target_colname = 'dst_mode'
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# , include_gene_name = False)
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